R Observatory: Metrics for TKApprox version 0.1.0

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

TKApprox

0.1.0Package record

A General Framework for Bayesian Estimation Using the 'Tierney'-'Kadane' Approximation

0packages depend

566downloads / year

48.8%test coverage

13/13flavors OK

Overview

About

Maintained by Shikhar TyagiCRAN page ↗

Provides a distribution-independent framework for Bayesian estimation of arbitrary univariate probability models using the 'Tierney'-'Kadane' approximation ('Tierney' & 'Kadane', 1986 doi:10.1080/01621459.1986.10478240). Users specify the probability distribution, likelihood, prior distributions, and censoring mechanism, while the package automatically constructs the posterior distribution, computes posterior modes and Hessian matrices, approximates posterior expectations under several Bayesian loss functions, and returns Bayesian parameter estimates, posterior covariance matrices, credible intervals, diagnostic plots, and model comparison statistics. Supports complete, right-, left-, interval-, Type-I, Type-II, progressive Type-II, hybrid, and doubly censored data ('Lawless', 2003 <ISBN:978-0-471-37215-8>; 'Meeker' & 'Escobar', 1998 <ISBN:978-0-471-14328-4>; 'Balakrishnan' & 'Aggarwala', 2000 <ISBN:978-0-8176-4129-0>; 'Kundu' & 'Pradhan', 2009 doi:10.1198/TECH.2009.0019), making it a flexible tool for Bayesian reliability, survival, and lifetime data analysis.

Changes in 0.1.0

Initial Release

Features

Distribution-agnostic Bayesian estimation framework using Tierney-Kadane approximation
Support for arbitrary univariate probability distributions (continuous or discrete)
Multiple Bayesian loss functions:
Squared Error Loss (SEL) - posterior mean
LINEX Loss - asymmetric linear-exponential loss
General Entropy Loss (GEL) - generalized entropy loss
Precautionary Loss - conservative estimation
Weighted Squared Error Loss - inverse-variance weighting
Custom loss functions - user-defined

Censoring Support

Show all changes in 0.1.0Hide the rest of the changes in 0.1.0

Complete (uncensored) data
Right-censored data
Left-censored data
Interval-censored data
Type-I censoring
Type-II censoring
Progressive Type-II censoring
Hybrid censoring
Doubly censored data

Prior Distributions

Gamma prior
Normal prior
Beta prior
Uniform prior
Exponential prior
Log-Normal prior
Weibull prior
Inverse Gamma prior
User-defined priors (via custom functions)
Independent priors for multiple parameters

Optimization

Multiple backends: BFGS, L-BFGS-B, Nelder-Mead, nlminb, maxLik, trust
Automatic fallback between optimization methods
Convergence diagnostics and iteration tracking
Support for parameter bounds

Numerical Differentiation

Numerical gradients and Hessians via numDeriv
Optional analytic derivative support with validation

Model Comparison

AIC (Akaike Information Criterion)
BIC (Bayesian Information Criterion)
CAIC (Consistent AIC)
HQIC (Hannan-Quinn Information Criterion)
DIC (Deviance Information Criterion) approximation
Expected log-posterior
Negative log-likelihood

Visualization

Posterior approximation plots (per parameter)
Likelihood surface and profile likelihood plots
Contour plots for two-parameter models
Prior vs posterior comparison plots
Convergence diagnostics
Credible interval forest plots
Residuals plots (Pearson and deviance)

Prior Sensitivity Analysis

Systematic variation of prior hyperparameters
Impact on estimates, bias, variance, and risk
Sensitivity plots and tables

S3 Methods

summary() - comprehensive model summary
print() - concise output
coef() - extract Bayes estimates
vcov() - extract posterior covariance matrix
logLik() - extract log-likelihood
AIC() - compute AIC
BIC() - compute BIC
plot() - diagnostic plots
predict() - generate predictions
residuals() - compute residuals

Testing

Comprehensive testthat test suite
Tests for all censoring schemes
Tests for multiple prior families
Tests for loss functions
Tests for optimization scenarios
GitHub Actions CI/CD for cross-platform checking

Documentation

roxygen2 documentation for all exported functions
Runnable examples in documentation
Comprehensive vignettes (planned)

From NEWS.md in the release

Install

R (CRAN)Ubuntu (r2u)Fedora (COPR)openSUSE (OBS)

install.packages("TKApprox", repos = "https://cloud.r-project.org/")

Installs the current CRAN release. Prebuilt binaries on Windows and macOS.

Release

# enable cranapt (r2u) if it is not already set up, then install
grep -rqs r2u.stat.illinois.edu /etc/apt/sources.list /etc/apt/sources.list.d/ \
  || curl -fsSL https://raw.githubusercontent.com/eddelbuettel/r2u/master/inst/scripts/add_cranapt_noble.sh | sudo bash
sudo apt install -y r-cran-tkapprox

Fast Ubuntu binaries via r2u (no compilation). The cranapt setup runs once and is skipped when it is already enabled. We list this channel from recorded r2u download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

bspm (used by r2u) also needs make: sudo apt install make

# enable the iucar/cran COPR if it is not already present, then install
ls /etc/yum.repos.d/*iucar*cran*.repo >/dev/null 2>&1 \
  || sudo dnf copr enable -y iucar/cran
sudo dnf install -y R-CRAN-TKApprox

RPMs from the iucar/cran COPR, rebuilt from CRAN.

For install.packages() integration in R, also: sudo dnf install R-CoprManager

# add the autoCRAN OBS repo (Tumbleweed) if it is missing, then install
zypper -q lr | grep -qi autoCRAN \
  || sudo zypper addrepo https://download.opensuse.org/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Tumbleweed/devel:languages:R:autoCRAN.repo
sudo zypper install -y R-TKApprox

RPMs auto-built from CRAN on openSUSE OBS (Tumbleweed). We list this channel from recorded autoCRAN download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

Health

CRAN checks

13OK

CRAN check page ↗

Slowest check: 4.0 min · r-oldrel-macos-x86_64

Status changes

OK13 OKNow
OK6 OK2026-08-08

Advisories

none known

How this is measured·How advisories are found

Documentation

In release 0.1.0

READMEYes · 490 wordsVignettesYes · 8 · dynamicNEWSYes · 67% structured · mentions 0.1.0Citation fileNoWebsitenot declared in DESCRIPTION

Help pages: 62
Examples that run: 100%25 run in a plain check
Exported functions with examples: 56%
Documented parameters: 100%
Return-value docs: 100%
Help pages citing references: 0%

Vignettes (8)

Censoring Schemes HTML
Defining Custom Distributions HTML
Introduction to TKApprox HTML
Loss Functions HTML
Prior Specification HTML
Real Data Example HTML
Simulation Studies HTML
Tierney-Kadane Approximation: Theory and Derivation HTML

How these are measured

Downloads

566

CRAN downloads in the past year

Rank #22,981 · ~2/day · ~47/mo

Releases R versions

Daily download trend is not available in this view yet.

22230 days

56690 days

5661 year

Compare downloads with other packages →

Also on14 r2u1 autocran

How downloads are counted

Dependencies

Declared dependencies

10 external dependencies (excludes base and recommended)

Installing TKApprox adds 10 packages (5 with compiled code, 4.2 MB of source tarballs). Heaviest: sandwich 1.4 MB, maxLik 791 KB, rlang 766 KB.

Depends (1)

R >= 4.0.0

Imports (9)

checkmate numDeriv maxLik rlangstatsgraphicsgrDevicesutilsMASS

LinkingTo (0)

none

Suggests (6)

testthat knitr rmarkdown pkgdown ggplot2 gridExtra

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

0direct

0indirect

Nothing depends on this yet.

How these are counted

Code & Tests

People & History

People (4)

Shikhar Tyagi

Author, Maintainer since 0.1.0 (2026)

Arvind Pandey

Author

Bhupendra Singh

Author

Vrijesh Tripathi

Author

Releases (1)

0.1.0Latest
record
2026-08-07 · current release
R
R 4.6.0 released · 2026-04-24

Package metadata

First published: 2026-08-07
Total releases: 1 / 1 yrs
License: GPL (>= 3) OSI
Minimum R: ≥ 4.0.0
Download size: 232 KB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("TKApprox")

Format

Tyagi, S., Pandey, A., Singh, B., & Tripathi, V. (2026). TKApprox: A General Framework for Bayesian Estimation Using the 'Tierney'-'Kadane' Approximation (Version 0.1.0) [Computer software]. https://doi.org/10.32614/CRAN.package.TKApprox

This package has no citation file, so citation() returns this entry built from DESCRIPTION.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

Format

APA

Balamuta, J. J. (2026). R Observatory: Metrics for TKApprox version 0.1.0 [Data set]. HJJB, LLC. Data release v2026-10-21. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-21, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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